Learning to Manipulate beyond Imitation
Abstract:
Imitation learning has been a prevalent approach for teaching robots manipulation skills but still suffers from scalability and generalizability. In this talk, I’ll argue for going beyond elementary behavioral imitation from human demonstrations. Instead, I’ll present two key directions: 1) Creating Manipulation Controllers from Pre-Trained Representations, and 2) Representing Video Demonstrations with Parameterized Symbolic Abstraction Graphs.
Committee:
Abhinav Gupta
Chris Atkeson
Deepak Pathak
Sudeep Dasari